# Anti dependency distance minimization in short sequences. A graph theoretic approach

arXiv: https://arxiv.org/abs/1906.05765 · HTML: https://mlsanity.com/paper/1906.05765/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 13 of 13)

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- [The optimality of word lengths. Theoretical foundations and an empirical study](https://mlsanity.com/paper/2208.10384/) (2022)
- [Predictability maximization and the origins of word order harmony](https://mlsanity.com/paper/2408.16570/) (2024)
- [Who is the root in a syntactic dependency structure?](https://mlsanity.com/paper/2501.15188/) (2025)
- [Dependency distance minimization predicts compression](https://mlsanity.com/paper/2109.08900/) (2021)
- [Ease of dependency distance minimization in star-like structures](https://mlsanity.com/paper/2604.28034/) (2026)
- [Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations](https://mlsanity.com/paper/2609.06025/) (2026)

## Abstract (arXiv)

Dependency distance minimization (DDm) is a word order principle favouring the placement of syntactically related words close to each other in sentences. Massive evidence of the principle has been reported for more than a decade with the help of syntactic dependency treebanks where long sentences abound. However, it has been predicted theoretically that the principle is more likely to be beaten in short sequences by the principle of surprisal minimization (predictability maximization). Here we introduce a simple binomial test to verify such a hypothesis. In short sentences, we find anti-DDm for some languages from different families. Our analysis of the syntactic dependency structures suggests that anti-DDm is produced by star trees.